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Schematic representation of the multi-scale liver architecture. The human liver is divided grossly into four parts or lobes. The four lobes are the right lobe, the left lobe, the caudate lobe, and the quadrate lobe. Seen from the front the liver is divided into two lobes: the right lobe and the left lobe. It is further divided in eight functionally independent segments based in the the Couinaud classification of liver anatomy. At the microscopic (histological) scale, the liver is organized in repetitive functional units called liver lobules, which take the shape of polygonal prisms (typically hexagonal in cross section). Each lobule is mainly constituted by hepatocytes and it is centered on a branch of the hepatic vein called the central vein which is interconnected with the interlobular portal triads: the hepatic artery (red), the portal vein (blue), bile duct (green). https://doi.org/10.1371/journal.pcbi.1010920.g001
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We present a multiagent-based model that captures the interactions between different types of cells with their microenvironment, and enables the analysis of the emergent global behavior during tissue regeneration and tumor development. Using this model, we are able to reproduce the temporal dynamics of regular healthy cells and cancer cells, as wel...
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... liver is a highly complex organ, which removes drugs and toxins from the blood. It is characterized by its multi-scale architecture (Fig 1) which consists of four lobes: the right lobe, the left lobe, the caudate lobe, and the quadrate lobe, which are further divided into eight segments based in the Couinaud system, also known as hepatic segments [65]. Each segment has its own vascular inflow, outflow and biliary drainage. ...
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... observe that, despite the initial location, there is a delay in the cancer cell reactivation in comparison to the liver regeneration process. Fig 10a shows the process in a qualitative fashion. Cancer cells start growing after the liver finishes its regeneration process. ...
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... this case, they grow inwards, following the increase of oxygen concentration, and consequently they proliferate towards the blood vessels (the animation of the HCC recurrence can be seen at the S3 Video). The growth of the tumor cells showed in Fig 10b, allowed us to compute the specific growth rate of the tumor (Eq 3), which give us α = 0.053%/day. By using the Gompertz Model (Eq 2), we can predict the tumor growth kinetics. ...
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... initial volume would be the tumor size at the end of the simulation which was V 0 = 0.0028 mm 3 , and the constant α is the specific growth rate. The prediction of Gompertz model is shown in Fig 10c as a blue dotted line. If we consider a detection size of 5 mm, the recurrence of the modeled tumor could be detected around the 95 th day (as shown in the inset of Fig 10c). ...
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... prediction of Gompertz model is shown in Fig 10c as a blue dotted line. If we consider a detection size of 5 mm, the recurrence of the modeled tumor could be detected around the 95 th day (as shown in the inset of Fig 10c). In Fig 10b, shaded regions represent the standard deviations of 40 simulations for liver regeneration and for tumor growth, while in Fig 10c shaded regions represent the Gompertz model calculated based on the standard deviation values. ...
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... we consider a detection size of 5 mm, the recurrence of the modeled tumor could be detected around the 95 th day (as shown in the inset of Fig 10c). In Fig 10b, shaded regions represent the standard deviations of 40 simulations for liver regeneration and for tumor growth, while in Fig 10c shaded regions represent the Gompertz model calculated based on the standard deviation values. This is in qualitative agreement with the clinical ...
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... we consider a detection size of 5 mm, the recurrence of the modeled tumor could be detected around the 95 th day (as shown in the inset of Fig 10c). In Fig 10b, shaded regions represent the standard deviations of 40 simulations for liver regeneration and for tumor growth, while in Fig 10c shaded regions represent the Gompertz model calculated based on the standard deviation values. This is in qualitative agreement with the clinical ...
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... have performed an exploratory sensitivity analysis by varying ±10% the input variables that feed our model: oxygen uptake of tumor cells and hepatocytes, hepatocytes and cancer cell cycles duration, repulsion and adhesion coefficients between cancer cells and hepatocytes. Fig 11 shows the tumor size relative change based on the variation of those parameters. The blue line represents the mean tumor size, the yellow shaded region represents the standard deviation of the ...
Citations
... Input parameters for the model and references for each parameter. Some of the parameters had been estimated in previous studies56 . ...
Chimeric antigen receptor (CAR) T-cell therapy is a promising immunotherapy for treating cancers. This method consists in modifying the patients’ T-cells to directly target antigen-presenting cancer cells. One of the barriers to the development of this type of therapies, is target antigen heterogeneity. It is thought that intratumour heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model (ABM), built upon a previous ABM, to rationalise the outcomes of different CAR T-cells therapies strategies over heterogeneous tumour-derived organoids. We found that one dose of CAR T-cell therapy should be expected to reduce the tumour size as well as its growth rate, however it may not be enough to completely eliminate it. Moreover, the amount of free CAR T-cells (i.e. CAR T-cells that did not kill any cancer cell) increases as we increase the dosage, and so does the risk of side effects. We tested different strategies to enhance smaller dosages, such as enhancing the CAR T-cells long-term persistence and multiple dosing. For both approaches an appropriate dosimetry strategy is necessary to produce “effective yet safe” therapeutic results. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of cells with low antigen expression. This shield turns out to protect cells with high antigen expression. Finally we tested a multi-antigen recognition therapy to overcome antigen escape and heterogeneity. Our studies suggest that larger dosages can completely eliminate the organoid, however the multi-antigen recognition increases the risk of side effects. Therefore, an appropriate small dosages dosimetry strategy is necessary to improve the outcomes. Based on our results, it is clear that a proper therapeutic strategy could enhance the therapies outcomes. In that direction, our computational approach provides a framework to model treatment combinations in different scenarios and to explore the characteristics of successful and unsuccessful treatments.
... The model presented herein builds upon previous work by Luque et al. on tissue growth kinetics [30]. The following subsections will briefly recall details of the mentioned model. ...
One of the barriers to the development of effective adoptive cell transfer therapies (ACT), specifically for genetically engineered T-cell receptors (TCRs), and chimeric antigen receptor (CAR) T-cells, is target antigen heterogeneity. It is thought that intratumor heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model to rationalize the outcomes of two types of ACT therapies over heterogeneous tumors: antigen specific ACT therapy and multi-antigen recognition ACT therapy. We found that one dose of antigen specific ACT therapy should be expected to reduce the tumor size as well as its growth rate, however it may not be enough to completely eliminate it. A second dose also reduced the tumor size as well as the tumor growth rate, but, due to the intratumor heterogeneity, it turned out to be less effective than the previous dose. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of cells with low oncoprotein expression. This shield turns out to protect cells with high oncoprotein expression. On the other hand, our studies suggest that the earlier the multi-antigen recognition ACT therapy is applied, the more efficient it turns. In fact, it could completely eliminate the tumor. Based on our results, it is clear that a proper therapeutic strategy could enhance the therapies outcomes. In that direction, our computational approach provides a framework to model treatment combinations in different scenarios and explore the characteristics of successful and unsuccessful treatments.
